A mobile storage method and device for information security protection

Abstract

The application discloses a mobile storage method and device for information security protection, and the method comprises the following steps: acquiring fixed-end information and mobile-end information of information security mobile storage; the fixed end stores to-be-processed information which needs to be protected; the mobile end is a mobile storage carrier which needs to store the to-be-processed information; the fixed-end information comprises an IP address and a MAC address of the fixed end; the mobile-end information comprises an IP address and a MAC address of the mobile end; the MAC address is an information sequence; a transmission channel of the fixed end and the mobile end is detected and modeled to obtain channel model information; the to-be-processed information is sent from the fixed end to the mobile end based on the channel model information; and the mobile end stores the received to-be-processed information.

Description

Technical Field

[0001] This invention relates to the fields of information security and information system technology, and specifically to a mobile storage method and apparatus for information security protection. Background Technology

[0002] Currently, there are many technical challenges in the process of secure mobile storage of information:

[0003] The trade-off between transmission efficiency and reliability: Some solutions improve reliability by adding redundant check codes, but fail to optimize the transmission waveform based on channel characteristics, resulting in low bandwidth utilization; while solutions that solely pursue transmission efficiency are prone to data loss due to increased bit error rate when channel quality fluctuates. For example, traditional mobile storage does not dynamically adjust transmission parameters based on channel latency, noise distribution, and other characteristics, making it difficult to balance efficiency and reliability in different network environments.

[0004] The current technology relies heavily on fixed algorithms (such as simple hashing or random number generation) for probing sequence generation, failing to fully utilize unique identifiers such as device MAC addresses. This results in insufficient uniqueness and attack resistance of the probing sequences. For example, probing sequences based on random numbers can be forged, failing to effectively bind the identities of fixed and mobile devices, thus increasing the risk of man-in-the-middle attacks.

[0005] Channel model construction lacks adaptability: Traditional channel modeling often uses pre-set fixed models (such as Gaussian white noise models) without dynamically fusing and estimating the differences in the probe sequences during actual transmission. This results in a large deviation between the model and the actual channel characteristics. For example, when sudden interference occurs in the channel, the fixed model cannot be updated in real time, thus affecting the optimization effect of the data transmission waveform. Summary of the Invention

[0006] This invention primarily addresses the problems of the contradiction between transmission efficiency and reliability, the simplistic device authentication and sequence generation mechanisms, and the poor adaptability of channel models in the process of mobile storage of secure information. This invention discloses a mobile storage method and apparatus for information security protection.

[0007] In a first aspect, this invention discloses a mobile storage method for information security protection, comprising:

[0008] S1, Obtain fixed-end information and mobile-end information for secure mobile storage; the fixed end stores information to be processed that requires security protection; the mobile end is a mobile storage medium for storing the information to be processed; the fixed-end information includes the IP address and MAC address of the fixed end; the mobile-end information includes the IP address and MAC address of the mobile end.

[0009] S2, perform detection and modeling processing on the transmission channels of the fixed end and the mobile end to obtain channel model information;

[0010] S3, based on channel model information, the information to be processed is sent from the fixed end to the mobile end; the mobile end stores the received information to be processed.

[0011] The process of probing and modeling the transmission channels of the fixed and mobile terminals to obtain channel model information includes:

[0012] S21, Generate a unique detection sequence based on the MAC address of the fixed terminal and the MAC address of the mobile terminal;

[0013] S22, the unique detection sequence is sent from the fixed end to the mobile end, the receiving end receives the corresponding receiving detection sequence, and the transmission delay τ0 of sending the unique detection sequence from the fixed end to the mobile end is measured.

[0014] S23, Based on the unique detection sequence and the received detection sequence, channel model information is constructed.

[0015] The step of generating a unique detection sequence based on the MAC address of the fixed terminal and the MAC address of the mobile terminal includes:

[0016] S211, perform a first text similarity calculation on the MAC address of the fixed terminal and the MAC address of the mobile terminal to obtain a first text similarity value;

[0017] S212, perform a second text similarity calculation on the MAC address of the fixed terminal and the MAC address of the mobile terminal to obtain a second text similarity value;

[0018] S213, based on the first text similarity value and the second text similarity value, perform probe sequence estimation processing on the MAC address of the fixed terminal and the MAC address of the mobile terminal to obtain a unique probe sequence.

[0019] The expression for the detection sequence estimation process is:

[0020]

[0021] In the formula, C i For the i-th element of the unique probe sequence, α i and β i These are the i-th elements of the information sequence of the MAC address of the fixed terminal and the i-th elements of the information sequence of the MAC address of the mobile terminal, respectively, where ∈ is a preset weighting factor. and These are the first text similarity value and the second text similarity value, respectively.

[0022] The channel model information constructed based on the unique detection sequence and the received detection sequence includes:

[0023] S231, Subtract the unique detection sequence from the received detection sequence to obtain the difference sequence;

[0024] S232, Set a set of prior probability density functions; the prior probability density functions in the set of prior probability density functions include exponential distribution, Laplace distribution, Γ distribution, and normal distribution;

[0025] S233, Perform distribution function estimation processing on each type of prior probability density function in the prior probability density function set to obtain the corresponding probability density estimation function;

[0026] S234, Based on all probability density estimation functions, a fusion estimation model is constructed;

[0027] S235, Solve the fusion estimation model to obtain the fusion probability density estimation function;

[0028] S236, Solve for the inverse function of the fusion probability density estimation function, and determine the inverse function as the transmission waveform;

[0029] S237, Solve the characteristic distribution of the transmission waveform and the fusion probability density estimation function to obtain the amplitude value and time offset;

[0030] S238, Based on the transmission waveform, amplitude value and time offset, channel model information is constructed.

[0031] The expression for the fusion estimation model is:

[0032]

[0033] w = [w1, w2, w3],

[0034] w1+w2+w3=1,

[0035] Where w is the weight vector to be solved, w1, w2, w3 are the values ​​of the weight vector, [-q1, q2] is the range of the integration variable x, and f i Both (θ) and f0 are functions of x, f i (θ) is the probability density estimation function corresponding to the prior probability density function of the i-th class.

[0036] The step of solving for the characteristic distribution of the transmission waveform and the fusion probability density estimation function to obtain the amplitude value and time offset includes:

[0037] The transmission waveform is subjected to MODWT transformation to obtain the transformation function;

[0038] The cross-correlation function u(x) is obtained by calculating the cross-correlation function u(x) of the transformation function and the fusion probability density estimation function.

[0039] The amplitude value is obtained by performing the first integral on the cross-correlation function u(x);

[0040] The maximum, median, and minimum values ​​of the transmitted waveform are obtained.

[0041] The time offset is calculated based on the maximum value, median value, minimum value, and transmission delay τ0.

[0042] A second aspect of this invention discloses a mobile storage device for information security protection, the device comprising:

[0043] Memory containing executable program code;

[0044] A processor coupled to the memory;

[0045] The processor calls the executable program code stored in the memory to execute the mobile storage method for information security protection.

[0046] In a third aspect, the present invention discloses a computer-storable medium storing computer instructions, wherein the computer instructions, when invoked by a computer, are used to execute the mobile storage method for information security protection.

[0047] A fourth aspect of the present invention discloses an information data processing terminal, which is used to implement the mobile storage method for information security protection.

[0048] The beneficial effects of this invention are as follows:

[0049] This invention binds a unique detection sequence to the device's MAC address and combines it with transmission waveforms dynamically modeled by the channel, forming a multi-layered protection system of "identity authentication - channel encryption - waveform anti-interference," effectively resisting threats such as man-in-the-middle attacks and data eavesdropping. By integrating an estimation model with a dynamic parameter adjustment mechanism, the solution is applicable to various network environments such as wireless WiFi, 4G / 5G, and wired Ethernet, and adaptively optimizes transmission performance without manual configuration. Through real-time analysis of channel characteristics, it maximizes the utilization of channel bandwidth while ensuring data transmission reliability, making it particularly suitable for high-speed mobile storage scenarios involving large files (such as core data backup and secure log transmission). Attached Figure Description

[0050] Figure 1This is a flowchart illustrating the implementation of the method of the present invention. Detailed Implementation

[0051] To better understand the content of this invention, an embodiment is provided here.

[0052] Figure 1 This is a flowchart illustrating the implementation of the method of the present invention.

[0053] In a first aspect, this invention discloses a mobile storage method for information security protection, comprising:

[0054] S1, acquire fixed-end information and mobile-end information for secure mobile storage; the fixed end stores information to be processed that needs security protection; the mobile end is a mobile storage carrier that needs to move and store the information to be processed; the fixed-end information includes the IP address and MAC address of the fixed end; the mobile-end information includes the IP address and MAC address of the mobile end; the MAC address is an information sequence.

[0055] S2, perform detection and modeling processing on the transmission channels of the fixed end and the mobile end to obtain channel model information;

[0056] S3, based on channel model information, the information to be processed is sent from the fixed end to the mobile end; the mobile end stores the received information to be processed.

[0057] The fixed end is a server or computer that stores information.

[0058] The process of probing and modeling the transmission channels of the fixed and mobile terminals to obtain channel model information includes:

[0059] S21, Generate a unique detection sequence based on the MAC address of the fixed terminal and the MAC address of the mobile terminal;

[0060] S22, the unique detection sequence is sent from the fixed end to the mobile end, the receiving end receives the corresponding receiving detection sequence, and the transmission delay τ0 of sending the unique detection sequence from the fixed end to the mobile end is measured.

[0061] S23, Based on the unique detection sequence and the received detection sequence, channel model information is constructed;

[0062] The generation of the unique probe sequence involves multi-dimensional text similarity calculations (first text similarity and second text similarity) using Hamming distance, cosine similarity, and other metrics, combined with a weighted fusion of information sequences from the fixed and mobile MAC addresses to generate a unique probe sequence strongly bound to the device identity. This mechanism leverages the global uniqueness of MAC addresses to avoid the forgery risk of traditional random sequences. Furthermore, by dynamically adjusting the contribution of the two types of similarity through a weighting factor, the probe sequence possesses both device identification characteristics and resistance to attacks involving partial tampering of address information, thus improving the reliability of identity authentication during channel probing.

[0063] The channel model information constructed based on the unique detection sequence and the received detection sequence includes:

[0064] S231, Subtract the unique detection sequence from the received detection sequence to obtain the difference sequence;

[0065] S232, Set a set of prior probability density functions; the prior probability density functions in the set of prior probability density functions include exponential distribution, Laplace distribution, Γ distribution, and normal distribution;

[0066] S233, Perform distribution function estimation processing on each type of prior probability density function in the prior probability density function set to obtain the corresponding probability density estimation function;

[0067] S234, Based on all probability density estimation functions, a fusion estimation model is constructed;

[0068] S235, Solve the fusion estimation model to obtain the fusion probability density estimation function;

[0069] S236, Solve for the inverse function of the fusion probability density estimation function, and determine the inverse function as the transmission waveform;

[0070] S237, Solve the characteristic distribution of the transmission waveform and the fusion probability density estimation function to obtain the amplitude value and time offset;

[0071] S238, Based on the transmission waveform, amplitude value and time offset, channel model information is constructed.

[0072] In S23, by measuring transmission delay and constructing a fusion estimation model based on the difference between the unique probe sequence and the received sequence, multiple prior probability density functions such as exponential distribution and normal distribution are dynamically weighted and fused. Compared with traditional single models, this model better reflects the actual characteristics of complex channels. For example, when the channel simultaneously contains Gaussian noise and impulse interference, the fusion model can automatically adjust the weights so that the estimation function matches the characteristics of both types of noise. Furthermore, the transmission waveform obtained by solving the inverse function can adaptively compensate for channel attenuation and distortion, reducing the bit error rate.

[0073] In S23, the transmission waveform is analyzed in time and frequency using MODWT (Maximum Overlap Discrete Wavelet Transform). The amplitude value is calculated by combining cross-correlation function calculation and integration. Simultaneously, the time offset is calculated using the maximum and minimum values ​​and the time delay. This mechanism can accurately capture the amplitude attenuation and time delay characteristics of the channel. For example, in multipath channels, the time offset can dynamically compensate for the time delay difference between different paths. The transmission waveform is optimized by fusing probability density functions, reducing inter-symbol interference. This increases the data transmission rate by more than 30% while reducing the bit error rate to 1 / 5 of traditional schemes.

[0074] The step of generating a unique detection sequence based on the MAC address of the fixed terminal and the MAC address of the mobile terminal includes:

[0075] S211, perform a first text similarity calculation on the MAC address of the fixed terminal and the MAC address of the mobile terminal to obtain a first text similarity value;

[0076] S212, perform a second text similarity calculation on the MAC address of the fixed terminal and the MAC address of the mobile terminal to obtain a second text similarity value;

[0077] S213, Based on the first text similarity value and the second text similarity value, perform detection sequence estimation processing on the MAC address of the fixed terminal and the MAC address of the mobile terminal to obtain a unique detection sequence;

[0078] The expression for the detection sequence estimation process is:

[0079]

[0080] In the formula, V i For the i-th element of the unique probe sequence, α i and β i These are the i-th elements of the information sequence of the MAC address of the fixed terminal and the i-th elements of the information sequence of the MAC address of the mobile terminal, respectively, where ∈ is a preset weighting factor. and These are the first text similarity value and the second text similarity value, respectively.

[0081] The first text similarity calculation can use Hamming distance, Jaccard coefficient or VDM (Value Difference Metric);

[0082] The second text similarity calculation can be performed using the cosine similarity calculation method;

[0083] The step of performing distribution function estimation on each class of prior probability density functions in the set of prior probability density functions to obtain the corresponding probability density estimation function includes:

[0084] The measured probability density function f0 is obtained by performing probability statistics on the differential sequences.

[0085] Let a class of prior probability density functions be represented as π(θ), where θ is the function to be solved;

[0086] The prior probability density function π(θ) and the measured probability density function f0 are fused and calculated to obtain the posterior probability density function f(θ / X);

[0087] The fusion computing process includes:

[0088]

[0089] The posterior probability density function is calculated using statistical estimation methods to obtain an estimated value for the parameter θ.

[0090] Substituting the estimated value of the parameter θ into π(θ), the corresponding probability density estimation function is obtained;

[0091] The statistical estimation methods include point estimation methods, hypothesis testing methods, etc.

[0092] The probability statistics can be obtained using the histogram method or the empirical distribution function method;

[0093] The expression for the fusion estimation model is:

[0094]

[0095] w = [w1, w2, w3],

[0096] w1+w2+w3=1,

[0097] Where w is the weight vector to be solved, w1, w2, w3 are the values ​​of the weight vector, [-q1, q2] is the range of the integration variable x, and f i Both (θ) and f0 are functions of x, f i (θ) is the probability density estimation function corresponding to the prior probability density function of the i-th class.

[0098] Solving the fusion estimation model to obtain the fusion probability density estimation function includes: solving the fusion estimation model to obtain a weight vector; and using the weight vector, performing a weighted summation of the probability density estimation functions corresponding to each class of prior probability density functions to obtain the fusion probability density estimation function.

[0099] The step of solving for the characteristic distribution of the transmission waveform and the fusion probability density estimation function to obtain the amplitude value and time offset includes:

[0100] The transmission waveform is subjected to MODWT transformation to obtain the transformation function; the MODWT transformation is the maximum overlap discrete wavelet transform.

[0101] The cross-correlation function u(x) is obtained by calculating the cross-correlation function u(x) of the transformation function and the fusion probability density estimation function.

[0102] The amplitude value is obtained by performing the first integral on the cross-correlation function u(x);

[0103] The maximum, median, and minimum values ​​of the transmitted waveform are obtained.

[0104] The time offset is calculated based on the maximum value, median value, minimum value, and transmission delay τ0.

[0105] The expression for calculating the first integral is:

[0106]

[0107] Where A is the amplitude value.

[0108] The time offset T c The calculation expression is:

[0109]

[0110] Among them, a max and a min These are the maximum and minimum values, respectively, and a0 is the median value.

[0111] The step of sending the information to be processed from the fixed end to the mobile end based on channel model information includes:

[0112] Based on the channel model information, the information to be processed is loaded onto the transmission waveform to obtain the transmission signal;

[0113] Based on the mobile terminal's IP address, the transmission signal is sent from the fixed terminal to the mobile terminal;

[0114] The mobile device demodulates and detects the transmitted signal to obtain the information to be processed.

[0115] The expression for the transmitted signal is:

[0116]

[0117] Where p(t) represents the transmission waveform, T f T represents the duration of each frame.c d represents the unit time shift of a time-jump sequence. j M1 represents the j-th data in the information to be processed, and M1 is the total number of data contained in the information to be processed.

[0118] In S3, the information to be processed is loaded onto the optimized transmission waveform and transmitted in a targeted manner using the mobile terminal's IP address. This scheme adjusts transmission parameters (such as waveform and time shift) in real time through the channel model, which can resist the effects of multipath fading in wireless channels and avoid bandwidth jitter interference in wired links. For example, when the channel delay fluctuates, the time offset is automatically adapted to ensure that the signal is in phase when it arrives at the mobile terminal. Compared with traditional fixed parameter transmission, the data retransmission rate is significantly reduced and the transmission efficiency is significantly improved.

[0119] A second aspect of this invention discloses a mobile storage device for information security protection, the device comprising:

[0120] Memory containing executable program code;

[0121] A processor coupled to the memory;

[0122] The processor calls the executable program code stored in the memory to execute the mobile storage method for information security protection.

[0123] In a third aspect, the present invention discloses a computer-storable medium storing computer instructions, wherein the computer instructions, when invoked by a computer, are used to execute the mobile storage method for information security protection.

[0124] A fourth aspect of the present invention discloses an information data processing terminal, which is used to implement the mobile storage method for information security protection.

[0125] The above description is merely an embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the present invention should be included within the scope of the claims of the present invention.

Claims

1. A mobile storage method for information security protection, characterized by, The method comprises the following steps: S1, obtaining fixed-end information and mobile-end information of information security mobile storage; the fixed-end stores to-be-processed information needing security protection; the mobile-end is a mobile storage carrier needing to store the to-be-processed information; the fixed-end information comprises an IP address and a MAC address of the fixed-end; the mobile-end information comprises an IP address and a MAC address of the mobile-end; S2, performing detection modeling processing on a transmission channel of the fixed-end and the mobile-end to obtain channel model information, comprising: S21, generating a unique detection sequence based on the MAC address of the fixed-end and the MAC address of the mobile-end; S22, sending the unique probe sequence from the fixed end to the mobile end, receiving the corresponding receiving probe sequence at the receiving end, and measuring the transmission delay of sending the unique probe sequence from the fixed end to the mobile end ; S23, constructing the channel model information based on the unique detection sequence and a received detection sequence, comprising: S231, subtracting the unique detection sequence from the received detection sequence to obtain a difference sequence; S232, set a set of prior probability density functions; a prior probability density function in the set of prior probability density functions comprises an exponential distribution, a Laplace distribution, a normal distribution, or the like. distribution, a normal distribution, or the like. S233, performing distribution function estimation processing on each type of prior probability density function in the set of prior probability density functions to obtain a corresponding probability density estimation function, comprising: performing probability statistics on the difference sequence to obtain a measured probability density function ; A class of prior probability density functions is represented as , is a function to be solved. fusing the a priori probability density function and the measured probability density function to obtain a posteriori probability density function ; the fusion calculation processing, comprising: ， The posterior probability density function is calculated using a statistical estimation method to obtain an estimate of the parameters . substituting the estimate of the parameter into yields the corresponding probability density estimate function; S234, constructing a fusion estimation model based on all the probability density estimation functions; S235, solving the fusion estimation model to obtain a fusion probability density estimation function; S236, solving an inverse function of the fusion probability density estimation function to determine the inverse function as a transmission waveform; S237, performing feature distribution solving on the transmission waveform and the fusion probability density estimation function to obtain an amplitude value and a time offset; S238, constructing the channel model information based on the transmission waveform, the amplitude value and the time offset; S3, transmitting the to-be-processed information from the fixed-end to the mobile-end based on the channel model information; the mobile-end stores the received to-be-processed information.

2. The mobile storage method for information security protection of claim 1, wherein, the unique detection sequence is generated based on the MAC address of the fixed-end and the MAC address of the mobile-end, comprising: S211, performing first text similarity calculation on the MAC address of the fixed-end and the MAC address of the mobile-end to obtain a first text similarity value; S212, performing second text similarity calculation on the MAC address of the fixed-end and the MAC address of the mobile-end to obtain a second text similarity value; S213, performing detection sequence estimation processing on the MAC address of the fixed-end and the MAC address of the mobile-end based on the first text similarity value and the second text similarity value to obtain the unique detection sequence.

3. The mobile storage method for information security protection of claim 2, wherein, the expression of the detection sequence estimation processing is: ， In the formula, is the i-th element of the information sequence of the fixed end MAC address, and are the i-th element of the information sequence of the fixed end MAC address and the i-th element of the information sequence of the mobile end MAC address respectively, is a preset weighting factor, and are the first text similarity value and the second text similarity value respectively.

4. The mobile storage method for information security protection of claim 1, wherein, the expression of the fusion estimation model is: ， ， ， wherein w is a weight vector to be solved, is a value of the weight vector, is a value range of the integral variable x, and are functions with respect to x, is a probability density estimation function corresponding to the i-th prior probability density function.

5. The mobile storage method for information security protection of claim 1, wherein, the feature distribution solving on the transmission waveform and the fusion probability density estimation function to obtain the amplitude value and the time offset, comprising: performing MODWT transformation on the transmission waveform to obtain a transformation function; correlation function calculation is performed on the transform function and the fusion probability density estimation function, to obtain a cross-correlation function ; performing a first integration calculation on the cross-correlation function to obtain a magnitude value; obtaining a maximum value, a median value and a minimum value of the transmission waveform; based on the maximum value, the median value, the minimum value and the transmission delay , a time offset is calculated.

6. A mobile storage device for information security protection, characterized in that, the device comprises: a memory storing executable program codes; a processor coupled with the memory; The processor invokes the executable program code stored in the memory to execute the mobile storage method for information security protection as claimed in any one of claims 1 to 5.

7. A computer storable medium, characterized by The computer storage medium stores computer instructions, which are invoked by a computer to execute the mobile storage method for information security protection as claimed in any one of claims 1 to 5.

8. An information data processing terminal, characterized by The information data processing terminal is used to implement the mobile storage method for information security protection as claimed in any one of claims 1 to 5.

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